By Aaron Brandwein, Chief Revenue Officer, Net Health
No matter where you are in the healthcare industry, the chances are high that you’ve heard about predictive analytics. And the chances are also high that unless you’re a computer scientist by trade, you’ve probably got some questions.
What does predictive analytics really entail? What’s the potential impact of these ideas on my area of healthcare? Are these concepts just a fad, or are they here to stay?
All of these are great questions to ask, especially if you’re a key leader or decision-maker that has to decide the direction of your practice, company, or organization. If it’s just a fad or not that impactful, these questions can save you from overextending or investing in a dead-end. But if it’s really a game-changer, these questions can help you understand how to get involved and how to protect your team from falling too far behind the curve.
In this article, I’d like to start by framing the discussion with a look at how predictive analytics are changing healthcare. And before we get started, I’d like to invite you to follow this account as we’re going to dig into the specifics, details, and actionable steps of predictive analytics, artificial intelligence (AI), and machine learning in follow-on posts in the coming weeks.
Clearing the Misconceptions
Many companies, clinicians, and researchers speak about predictive analytics and it can seem excessively academic. Yet practical-minded vendors like Net Health take a very different approach. Organizations that follow our lead start by looking at a few key things that predictive analytics are not to better frame the discussion. Let’s start today by looking at a few key things that predictive analytics are not to better frame the discussion.
They are not designed to replace clinicians. They’re created to augment the quality of care that a living, breathing clinician can deliver.
They don’t require a master’s degree in computer science to use. While the technology behind the scenes may be complex, the benefits quickly fade if it’s not designed for use without an excessive need for training.
These solutions are not a far-off dream or merely a theoretical concept. Predictive analytics are already being employed by healthcare providers at all levels to solve a myriad of problems.
So, What Are Predictive Analytics in Healthcare?
Now that we’ve cleared a few of the misconceptions out of the way, let’s look at a quick definition to make sure we’re all on the same page. Predictive analytics refers to the use of software, systems, algorithms, artificial intelligence (AI), and machine learning to analyze data to drive results, identify trends, and aid in decision making.
In other words and as it relates to healthcare—it’s the use of computer systems to help humans assess more variables faster than any human can on their own to better inform stakeholders throughout the healthcare ecosystem of potential risks and opportunities and guide them to better decisions.
How This Stands to Change Healthcare
So, what does all of this look like in practice? What are some real-world benefits that we stand to gain from predictive analytics? Let’s look at a few examples.
Improved Patient Outcomes – Predictive analytics can help to analyze hundreds of different patient factors and millions of past cases and results to help make more accurate and faster diagnoses, identify population health trends and crises early, and give clinicians more tools to plan better treatment plans. The result? Greatly improved patient outcomes.
More Efficient Operations – Predictive analytics doesn’t just help with patients, but they can also help with logistics and operations behind the scenes. Some of the ways include automatic identification and resolution of billing issues, automatic schedule optimization, and a reduction in documentation time. These benefit the operations, but simultaneously also improve patient outcomes.
This is just the tip of the iceberg when it comes to the benefits of predictive analytics in healthcare, but it’s a pretty big iceberg. If we can dramatically improve patient outcomes while lightening the load on our staff and while driving a stronger bottom line—that’s a major win.
Remember to stay tuned if you’re interested in learning more about predictive analytics in healthcare. I’ll be releasing several blogs in the coming weeks and months talking in detail about actionable steps for healthcare organizations and practices, big and small.